Crowd Sourcing of Device Sensor Data for Real Time Response

ABSTRACT

A system, method, and computer-readable medium for performing a crowdsourcing data analysis operation. More specifically, the crowdsourcing data analysis operation receives data from a plurality of crowd sourced devices, aggregates the data received from the plurality of crowd sourced devices and maps the data received from the plurality of crowd sourced devices to a cohort (i.e., a group of individuals used in a study who have something in common). In certain embodiments, the crowdsourcing data analysis operation analyzes the data received from the plurality of crowd sourced devices to provide a deterministic analysis to infer a likelihood of potential incidents related to a group of individuals at any given time. Additionally in certain embodiments, the mapping of the data received from the plurality of crowd sourced devices includes binding the data to a physical or logical location.

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates to information handling systems. Morespecifically, embodiments of the invention relate to crowd sourcing ofdevice sensor data for real time response.

Description of the Related Art

Early detection of events can be challenging, especially when the eventoccurs in a public venue and crowds are involved. It can be especiallydesirable to detect negative events in such a situation. Early detectionof negative events, or identifying the precursors for such events canhelp in preventing the occurrence of the negative event. Additionally,should such an event occur, early detection of the event cansignificantly reduce any negative impacts of the events and aid in aprompt response to the event. For example, with a performance such as amusic concert in a large venue, early detection of a crowd movingtowards exits of the venue at an unexpected time can provide anindication of panic. Such a detection might alert the venue staff toopen additional escape routes which in turn would likely help inminimizing injuries or even loss of life.

It is known to use a single sensor to detect the occurrence of an eventassociated with the single sensor. For example, services are availableto detect if a user falls and if so to trigger an emergency response.Also, it is known to integrate sensors into automobiles such that if anaccident is detected (such as via sensing that an airbag has beendeployed), location data and service request are automatically sent toan appropriate emergency service.

However, in a use case where a plurality of users are located in aspecific location, it would be desirable to monitor not only telemetrydata of the individual, but also of the larger cohort in real time toallow inference of potential incidents which may affect a wider grouprather than the individual. It would also be desirable to provide anability to infer a likelihood of minor, major and critical events fromcollected telemetry data. It would also be desirable to provide an earlywarning system to mitigate the consequences of potentially negativeevents.

SUMMARY OF THE INVENTION

A system, method, and computer-readable medium are disclosed forperforming a crowdsourcing data analysis operation. More specifically,the crowdsourcing data analysis operation receives data from a pluralityof crowd sourced devices, aggregates the data received from theplurality of crowd sourced devices and maps the data received from theplurality of crowd sourced devices to a cohort (i.e., a group ofindividuals used in a study who have something in common). In certainembodiments, the crowdsourcing data analysis operation analyzes the datareceived from the plurality of crowd sourced devices to provide adeterministic analysis to infer a likelihood of potential incidentsrelated to a group of individuals at any given time. Additionally incertain embodiments, the mapping of the data received from the pluralityof crowd sourced devices includes binding the data to a physical orlogical location. In certain embodiments, the logical location comprisesa social event. Additionally, in certain embodiments, the crowdsourcingdata analysis operation includes presenting a visual cue to illustratethe analysis of the data received from the plurality of crowd sourceddata via a graphical representation.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 shows a schematic diagram of a question prioritization system.

FIG. 2 shows a block diagram of a data processing system.

FIG. 3 shows a block diagram of a crowd sourcing environment.

FIG. 4 shows a flow chart of the operation of a crowd sourcing deviceanalysis system.

FIG. 5 shows a graphical representation of the operation of a crowdsourcing device analysis system in a venue.

FIG. 6 shows another graphical representation of the operation of acrowd sourcing device analysis system in a venue.

DETAILED DESCRIPTION

The present invention may be a system, a method, and/or a computerprogram product. In addition, selected aspects of the present inventionmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, micro-code,etc.) or an embodiment combining software and/or hardware aspects thatmay all generally be referred to herein as a “circuit,” “module” or“system.” Furthermore, aspects of the present invention may take theform of computer program product embodied in a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a dynamic or static random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), a magnetic storage device, a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server or cluster of servers. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion prioritization system 10 and question/answer (QA) system 100connected to a computer network 140. The QA system 100 includes aknowledge manager 104 that is connected to a knowledge base 106 andconfigured to provide question/answer (QA) generation functionality forone or more content users who submit across the network 140 to the QAsystem 100. To assist with efficient sorting and presentation ofquestions to the QA system 100, the prioritization system 10 may beconnected to the computer network 140 to receive user questions, and mayinclude a plurality of subsystems which interact with cognitive systems,like the knowledge manager 100, to prioritize questions or requestsbeing submitted to the knowledge manager 100.

The Named Entity subsystem 12 receives and processes each question 11 byusing natural language (NL) processing to analyze each question andextract question topic information contained in the question, such asnamed entities, phrases, urgent terms, and/or other specified termswhich are stored in one or more domain entity dictionaries 13. Byleveraging a plurality of pluggable domain dictionaries relating todifferent domains or areas (e.g., travel, healthcare, electronics, gameshows, financial services), the domain dictionary 11 enables criticaland urgent words (e.g., “threat level”) from different domains (e.g.,“travel”) to be identified in each question based on their presence inthe domain dictionary 11. To this end, the Named Entity subsystem 12 mayuse a Natural Language Processing (NLP) routine to identify the questiontopic information in each question. As used herein, “NLP” refers to thefield of computer science, artificial intelligence, and linguisticsconcerned with the interactions between computers and human (natural)languages. In this context, NLP is related to the area of human-computerinteraction and natural language understanding by computer systems thatenable computer systems to derive meaning from human or natural languageinput. For example, NLP can be used to derive meaning from ahuman-oriented question such as, “What is tallest mountain in NorthAmerica?” and to identify specified terms, such as named entities,phrases, or urgent terms contained in the question. The processidentifies key terms and attributes in the question and compares theidentified terms to the stored terms in the domain dictionary 13.

The Question Priority Manager subsystem 14 performs additionalprocessing on each question to extract question context information 15A.In addition or in the alternative, the Question Priority Managersubsystem 14 may also extract server performance information 15B for thequestion prioritization system 10 and/or QA system 100. In selectedembodiments, the extracted question context information 15A may includedata that identifies the user context and location when the question wassubmitted or received. For example, the extracted question contextinformation 15A may include data that identifies the user who submittedthe question (e.g., through login credentials), the device or computerwhich sent the question, the channel over which the question wassubmitted, the location of the user or device that sent the question,any special interest location indicator (e.g., hospital, public-safetyanswering point, etc.), or other context-related data for the question.The Question Priority Manager subsystem 14 may also determine or extractselected server performance data 15B for the processing of eachquestion. In selected embodiments, the server performance information15B may include operational metric data relating to the availableprocessing resources at the question prioritization system 10 and/or QAsystem 100, such as operational or run-time data, CPU utilization data,available disk space data, bandwidth utilization data, etc. As part ofthe extracted information 15A/B, the Question Priority Manager subsystem14 may identify the SLA or QoS processing requirements that apply to thequestion being analyzed, the history of analysis and feedback for thequestion or submitting user, and the like. Using the question topicinformation and extracted question context and/or server performanceinformation, the Question Priority Manager subsystem 14 is configured topopulate feature values for the Priority Assignment Model 16 whichprovides a machine learning predictive model for generating a targetpriority values for the question, such as by using an artificialintelligence (AI) rule-based logic to determine and assign a questionurgency value to each question for purposes of prioritizing the responseprocessing of each question by the QA system 100.

The Prioritization Manager subsystem 17 performs additional sort or rankprocessing to organize the received questions based on at least theassociated target priority values such that high priority questions areput to the front of a prioritized question queue 18 for output asprioritized questions 19. In the question queue 18 of the PrioritizationManager subsystem 17, the highest priority question is placed at thefront for delivery to the assigned QA system 100. In selectedembodiments, the prioritized questions 19 from the PrioritizationManager subsystem 17 that have a specified target priority value may beassigned to a specific pipeline (e.g., QA System 100A) in the QA systemcluster 100. As will be appreciated, the Prioritization Managersubsystem 17 may use the question queue 18 as a message queue to providean asynchronous communications protocol for delivering prioritizedquestions 19 to the QA system 100 such that the Prioritization Managersubsystem 17 and QA system 100 do not need to interact with a questionqueue 18 at the same time by storing prioritized questions in thequestion queue 18 until the QA system 100 retrieves them. In this way, awider asynchronous network supports the passing of prioritized questionsas messages between different computer systems 100A, 100B, connectingmultiple applications and multiple operating systems. Messages can alsobe passed from queue to queue in order for a message to reach theultimate desired recipient. An example of a commercial implementation ofsuch messaging software is IBM's Web Sphere MQ (previously MQ Series).In selected embodiments, the organizational function of thePrioritization Manager subsystem 17 may be configured to convertover-subscribing questions into asynchronous responses, even if theywere asked in a synchronized fashion.

The QA system 100 may include one or more QA system pipelines 100A,100B, each of which includes a computing device 104 (comprising one ormore processors and one or more memories, and potentially any othercomputing device elements generally known in the art including buses,storage devices, communication interfaces, and the like) for processingquestions received over the network 140 from one or more users atcomputing devices (e.g., 110, 120, 130) connected over the network 140for communication with each other and with other devices or componentsvia one or more wired and/or wireless data communication links, whereeach communication link may comprise one or more of wires, routers,switches, transmitters, receivers, or the like. In this networkedarrangement, the QA system 100 and network 140 may enablequestion/answer (QA) generation functionality for one or more contentusers. Other embodiments of QA system 100 may be used with components,systems, sub-systems, and/or devices other than those that are depictedherein.

In each QA system pipeline 100A, 100B, a prioritized question 19 isreceived and prioritized for processing to generate an answer 20. Insequence, prioritized questions 19 are dequeued from the shared questionqueue 18, from which they are de-queued by the pipeline instances forprocessing in priority order rather than insertion order. In selectedembodiments, the question queue 18 may be implemented based on a“priority heap” data structure. During processing within a QA systempipeline (e.g., 100A), questions may be split into many subtasks whichrun concurrently. A single pipeline instance can process a number ofquestions concurrently, but only a certain number of subtasks. Inaddition, each QA system pipeline may include a prioritized queue (notshown) to manage the processing order of these subtasks, with thetop-level priority corresponding to the time that the correspondingquestion started (earliest has highest priority). However, it will beappreciated that such internal prioritization within each QA systempipeline may be augmented by the external target priority valuesgenerated for each question by the Question Priority Manager subsystem14 to take precedence or ranking priority over the question start time.In this way, more important or higher priority questions can “fasttrack” through the QA system pipeline if it is busy with already-runningquestions.

In the QA system 100, the knowledge manager 104 may be configured toreceive inputs from various sources. For example, knowledge manager 104may receive input from the question prioritization system 10, network140, a knowledge base or corpus of electronic documents 106 or otherdata, a content creator 108, content users, and other possible sourcesof input. In selected embodiments, some or all of the inputs toknowledge manager 104 may be routed through the network 140 and/or thequestion prioritization system 10. The various computing devices (e.g.,110, 120, 130, 150, 160, 170) on the network 140 may include accesspoints for content creators and content users. Some of the computingdevices may include devices for a database storing the corpus of data asthe body of information used by the knowledge manager 104 to generateanswers to cases. The network 140 may include local network connectionsand remote connections in various embodiments, such that knowledgemanager 104 may operate in environments of any size, including local andglobal, e.g., the Internet. Additionally, knowledge manager 104 servesas a front-end system that can make available a variety of knowledgeextracted from or represented in documents, network-accessible sourcesand/or structured data sources. In this manner, some processes populatethe knowledge manager with the knowledge manager also including inputinterfaces to receive knowledge requests and respond accordingly.

In one embodiment, the content creator creates content in a document 106for use as part of a corpus of data with knowledge manager 104. Thedocument 106 may include any file, text, article, or source of data(e.g., scholarly articles, dictionary definitions, encyclopediareferences, and the like) for use in knowledge manager 104. Contentusers may access knowledge manager 104 via a network connection or anInternet connection to the network 140, and may input questions toknowledge manager 104 that may be answered by the content in the corpusof data. As further described below, when a process evaluates a givensection of a document for semantic content, the process can use avariety of conventions to query it from the knowledge manager. Oneconvention is to send a well-formed question. Semantic content iscontent based on the relation between signifiers, such as words,phrases, signs, and symbols, and what they stand for, their denotation,or connotation. In other words, semantic content is content thatinterprets an expression, such as by using Natural Language (NL)Processing. In one embodiment, the process sends well-formed questions(e.g., natural language questions, etc.) to the knowledge manager.Knowledge manager 104 may interpret the question and provide a responseto the content user containing one or more answers to the question. Insome embodiments, knowledge manager 104 may provide a response to usersin a ranked list of answers.

In some illustrative embodiments, QA system 100 may be the IBM Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The IBM Watson™ knowledge managersystem may receive an input question which it then parses to extract themajor features of the question, that in turn are then used to formulatequeries that are applied to the corpus of data. Based on the applicationof the queries to the corpus of data, a set of hypotheses, or candidateanswers to the input question, are generated by looking across thecorpus of data for portions of the corpus of data that have somepotential for containing a valuable response to the input question.

The IBM Watson™ QA system then performs deep analysis on the language ofthe input prioritized question 19 and the language used in each of theportions of the corpus of data found during the application of thequeries using a variety of reasoning algorithms. There may be hundredsor even thousands of reasoning algorithms applied, each of whichperforms different analysis, e.g., comparisons, and generates a score.For example, some reasoning algorithms may look at the matching of termsand synonyms within the language of the input question and the foundportions of the corpus of data. Other reasoning algorithms may look attemporal or spatial features in the language, while others may evaluatethe source of the portion of the corpus of data and evaluate itsveracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the IBM Watson™ QA system. Thestatistical model may then be used to summarize a level of confidencethat the IBM Watson™ QA system has regarding the evidence that thepotential response, i.e. candidate answer, is inferred by the question.This process may be repeated for each of the candidate answers until theIBM Watson™ QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. The QA system 100 thengenerates an output response or answer 20 with the final answer andassociated confidence and supporting evidence. More information aboutthe IBM Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the IBM Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

Types of information handling systems that can utilize QA system 100range from small handheld devices, such as handheld computer/mobiletelephone 110 to large mainframe systems, such as mainframe computer170. Examples of handheld computer 110 include personal digitalassistants (PDAs), personal entertainment devices, such as MP3 players,portable televisions, and compact disc players. Other examples ofinformation handling systems include pen, or tablet, computer 120,laptop, or notebook, computer 130, personal computer system 150, andserver 160. As shown, the various information handling systems can benetworked together using computer network 140. Types of computer network140 that can be used to interconnect the various information handlingsystems include Local Area Networks (LANs), Wireless Local Area Networks(WLANs), the Internet, the Public Switched Telephone Network (PSTN),other wireless networks, and any other network topology that can be usedto interconnect the information handling systems. Many of theinformation handling systems include nonvolatile data stores, such ashard drives and/or nonvolatile memory. Some of the information handlingsystems may use separate nonvolatile data stores (e.g., server 160utilizes nonvolatile data store 165, and mainframe computer 170 utilizesnonvolatile data store 175). The nonvolatile data store can be acomponent that is external to the various information handling systemsor can be internal to one of the information handling systems. Anillustrative example of an information handling system showing anexemplary processor and various components commonly accessed by theprocessor is shown in FIG. 2.

FIG. 2 illustrates an information handling system 202, moreparticularly, a processor and common components, which is a simplifiedexample of a computer system capable of performing the computingoperations described herein. Information handling system 202 includes aprocessor unit 204 that is coupled to a system bus 206. A video adapter208, which controls a display 210, is also coupled to system bus 206.System bus 206 is coupled via a bus bridge 212 to an Input/Output (I/O)bus 214. An I/O interface 216 is coupled to I/O bus 214. The I/Ointerface 216 affords communication with various I/O devices, includinga keyboard 218, a mouse 220, a Compact Disk—Read Only Memory (CD-ROM)drive 222, a floppy disk drive 224, and a flash drive memory 226. Theformat of the ports connected to I/O interface 216 may be any known tothose skilled in the art of computer architecture, including but notlimited to Universal Serial Bus (USB) ports.

The information handling system 202 is able to communicate with aservice provider server 252 via a network 228 using a network interface230, which is coupled to system bus 206. Network 228 may be an externalnetwork such as the Internet, or an internal network such as an EthernetNetwork or a Virtual Private Network (VPN). Using network 228, clientcomputer 202 is able to use the present invention to access serviceprovider server 252.

A hard drive interface 232 is also coupled to system bus 206. Hard driveinterface 232 interfaces with a hard drive 234. In a preferredembodiment, hard drive 234 populates a system memory 236, which is alsocoupled to system bus 206. Data that populates system memory 236includes the information handling system's 202 operating system (OS) 238and software programs 244.

OS 238 includes a shell 240 for providing transparent user access toresources such as software programs 244. Generally, shell 240 is aprogram that provides an interpreter and an interface between the userand the operating system. More specifically, shell 240 executes commandsthat are entered into a command line user interface or from a file.Thus, shell 240 (as it is called in UNIX®), also called a commandprocessor in Windows®, is generally the highest level of the operatingsystem software hierarchy and serves as a command interpreter. The shellprovides a system prompt, interprets commands entered by keyboard,mouse, or other user input media, and sends the interpreted command(s)to the appropriate lower levels of the operating system (e.g., a kernel242) for processing. While shell 240 generally is a text-based,line-oriented user interface, the present invention can also supportother user interface modes, such as graphical, voice, gestural, etc.

As depicted, OS 238 also includes kernel 242, which includes lowerlevels of functionality for OS 238, including essential servicesrequired by other parts of OS 238 and software programs 244, includingmemory management, process and task management, disk management, andmouse and keyboard management. Software programs 244 may include abrowser 246 and email client 248. Browser 246 includes program modulesand instructions enabling a World Wide Web (WWW) client (i.e.,information handling system 202) to send and receive network messages tothe Internet using HyperText Transfer Protocol (HTTP) messaging, thusenabling communication with service provider server 252. In variousembodiments, software programs 244 may also include a crowd sourcinganalysis module 250. In these and other embodiments, the crowd sourcinganalysis module 250 includes code for implementing the processesdescribed hereinbelow. In one embodiment, information handling system202 is able to download the crowd sourcing analysis module 250 from aservice provider server 252.

The hardware elements depicted in the information handling system 202are not intended to be exhaustive, but rather are representative tohighlight components used by the present invention. For instance, theinformation handling system 202 may include alternate memory storagedevices such as magnetic cassettes, Digital Versatile Disks (DVDs),Bernoulli cartridges, and the like. These and other variations areintended to be within the spirit, scope and intent of the presentinvention.

FIG. 3 is a block diagram of a crowd sourcing analysis environment 300implemented in accordance with an embodiment of the invention. Invarious embodiments, the operation of a plurality of devices aremonitored to perform a crowdsourcing data analysis operation. In variousembodiments, a crowd sourcing analysis system 301 is implemented toexecute on a user device 304 and to perform a crowd sourcing analysisoperation within the crowd sourcing analysis environment 300. In certainembodiments, the crowd sourcing analysis operation may be performed as ahardware operation, a software operation, or a combination thereof. Incertain embodiments, the crowd sourcing analysis system 301 includessome or all of the functions performed by the crowd sourcing analysismodule 250.

The crowdsourcing data analysis operation receives data from theplurality of crowd sourced devices, aggregates the data received fromthe plurality of crowd sourced devices and maps the data received fromthe plurality of crowd sourced devices to a cohort (i.e., a group ofindividuals used in a study who have something in common). In certainembodiments, the crowdsourcing data analysis operation analyzes the datareceived from the plurality of crowd sourced devices to provide adeterministic analysis to infer a likelihood of potential incidentsrelated to a group of individuals at any given time. Additionally incertain embodiments, the mapping of the data received from the pluralityof crowd sourced devices includes binding the data to a physical orlogical location. In certain embodiments, the logical location comprisesa social event. Additionally, in certain embodiments, the crowdsourcingdata analysis operation includes presenting a visual cue to illustratethe analysis of the data received from the plurality of crowd sourceddata via a graphical representation.

As used herein, a user device 304 refers to an information handlingsystem such as a personal computer, a laptop computer, a tabletcomputer, a personal digital assistant (PDA), a smart phone, a mobiletelephone, or other device that is capable of communicating andprocessing data. In various embodiments, the user device can include oneor more analysis applications 306. In various embodiments, the userdevice 304 includes a repository of crowd sourcing data 308. Also, incertain embodiments the repository of crowd sourcing data 308 includes acrowd sourcing data repository. In certain embodiments, the crowdsourcing data repository may include a crowd sourcing database. Also, incertain embodiments, the crowd sourcing analysis system 301 and thecrowd sourcing data 308 may be physically disparate. Also, in certainembodiments, the crowd sourcing analysis system 301 may include a crowdsourcing device agent which executes elsewhere within the crowd sourcinganalysis environment 300. Skilled practitioners of the art will realizethat many such embodiments are possible and the foregoing is notintended to limit the spirit, scope or intent of the invention.

In various embodiments, the user device 304 is used to communicate databetween the crowd sourcing analysis system 301 and a master crowdsourcing analysis data system 322, described in greater detail herein,through the use of a network 140. In certain embodiments, the mastercrowd sourcing analysis data system 322 includes a repository of mastercrowd sourcing analysis data 324, likewise described in greater detailherein. In certain embodiments, the master crowd sourcing analysis data324 is used when performing a crowd sourcing analysis operation ofreceived crowd sourced data. For example, in certain embodiments, themaster crowd sourcing analysis data 324 may include data relating to aplurality of venues as well as typical and atypical behaviors associatedwith each of the plurality of venues.

In various embodiments, the master crowd sourcing analysis data system322 can include one or more of a relations database management system(RDBMS), a data warehouse, and a not only structure query language(NoSQL) database. Also, in various embodiments, the master crowdsourcing analysis data system 322 can include one or more cloud baseddatabases (e.g., Cloud DB1, Cloud DB2, Cloud DB3, etc.) Skilledpractitioners of the art will realize that many such embodiments arepossible and the foregoing is not intended to limit the spirit, scope orintent of the invention.

In various embodiments, the network 140 may be a public network, such asthe Internet, a physical private network, a virtual private network(VPN), or any combination thereof. In certain embodiments, the network140 may be a wireless network, including a personal area network (PAN),based on technologies such as Bluetooth or Ultra Wideband (UWB). Invarious embodiments, the wireless network may include a wireless localarea network (WLAN), based on variations of the IEEE 802.11specification, often referred to as WiFi. In certain embodiments, thewireless network may include a wireless wide area network (WWAN) basedon an industry standard including various 3G technologies, includingevolution-data optimized (EVDO), IEEE 802.16 (WiMAX), wireless broadband(WiBro), high-speed downlink packet access (HSDPA), high-speed uplinkpacket access (HSUPA), and emerging fourth generation (4G) wirelesstechnologies. Skilled practitioners of the art will realize that manysuch embodiments are possible and the foregoing is not intended to limitthe spirit, scope or intent of the invention.

As used herein, a venue profile broadly refers to a profile of a venue(or plurality of venues such as related venues) that can be used as areference for predicted incidents relating to a particular venue. Invarious embodiments, the venue profile may be generated based upon datathat are crowdsourced from a plurality of devices, such as crowdsourceddevices ‘1’ 326 through ‘n’ 328, some or all of which are located withina venue 329. As used herein, crowdsourcing broadly refers to the processof obtaining needed services, content or other information by solicitingcontributions from a group of users, devices or systems. Skilledpractitioners of the art will be aware that crowdsourcing is often usedto subdivide tedious tasks, processes or operations across multiplecontributors, each of which adds a portion of value to the greaterresult. In various embodiments, each of the crowdsourced devices ‘1’ 326through ‘n’ 328 provides their respective data to the crowd sourcinganalysis system 301 as well as the master crowd sourcing analysis datasystem 322. Once received, they are stored in the repository of venueprofile data 308. In various embodiments, the network 140 is used by thecrowdsourced devices ‘1’ 326 through ‘n’ 328 to respectively providetheir data to the device 304. In various embodiments, the crowd sourceddata is also stored in the master crowd sourcing analysis datarepository 324.

Ongoing operations are then performed to monitor data generated via thecrowdsourced devices as well as other devices accessing the master crowdsourcing data analysis system 322. Skilled practitioners of the art willrecognize that many methods for monitoring queries are possible and theforegoing is not intended to limit the spirit, scope or intent of theinvention.

Ongoing operations are then performed to store the crowd sourced data asit is collected for subsequent comparison and analysis. The method bywhich the crowd sourced data is stored, and the format in which it isstored, is a matter of design choice. In various embodiments, thecollected data is stored in the repository of crowd sourced data 308. Incertain embodiments, a subset of the collected data is stored in therepository of data 308. For example, data associated with an ‘n’ numberof users of the environment may be selected for storage in therepository of data 308 where the users selected for storage may havecertain characteristics relevant to the venue analysis. For example, incertain embodiments, the users for which the data is stored (andpotentially analyzed) may be located within a particular sub-portion ofthe venue. Skilled practitioners of the art will recognize that manymethods for identifying a number of users for analysis are possible andthe foregoing is not intended to limit the spirit, scope or intent ofthe invention.

FIG. 4 shows a flow chart of the operation 400 of a crowd sourcingdevice analysis system. More specifically, the operation begins at step410 with individuals having respective data generating devices enteringa venue of interest. For example, the venue of interest could correspondto an arena in which a performance or sporting event is to occur. Invarious embodiments, the individuals are provided with a device such asa wristband or lanyard which contains the device for generating thecrowdsourced data. In other embodiments, the device may correspond to auser device as described herein.

Next, at step 420, each crowdsourced device 329 registers with a crowdsourcing data analysis system 301. In certain embodiments, the crowdsourcing data analysis system 301 may be contained either locally orremotely within an overall building management system. Additionally, invarious embodiments, the crowdsourced device may include additionalidentification information which is provided to the crowd sourcing dataanalysis system 301 when the device is registered. This additionalidentification information can indicate whether the individualassociated with the crowdsourced device 301 could potentially requirespecial attention such as whether the individual may be associated withcertain business, medical, political or historical criteria. Forexample, the individual associated with the crowdsourced device 329 mayhave a medical condition such as epilepsy or may be a preferred guestfor whom guidance to preferred seating may be indicated.

Next, at step 430, the crowd sourcing data analysis system 301 initiatestracking of the plurality of crowd sourced devices 301. Each of thecrowd sourced devices provides device data relating to the individualassociated with the crowd sourced data. This device data includes one ormore of a device location, a device speed, and a device height. Incertain embodiments, the device speed of travel is provided as part ofthe device data. The device location and device speed can also be usedto generate a device direction and speed of travel. Additionally incertain embodiments, the device data can also include temperature andlight intensity data. Also, in certain embodiments, the device data caninclude medical data of the individual associated with the device suchas heart rate and blood pressure. Certain crowd sourced devices may beconsidered enhanced crowd sourced devices if the device provides moredevice data than other devices. In certain applications, enhanced crowdsourced devices might be provided to individuals according to certaincriteria. For example, individuals with particular health conditions orcertain status (e.g., VIP's) may be provided enhanced crowd sourceddevices while other individuals are provided with crowd sourced deviceswhich provide a subset of the data provided by the enhanced crowdsourced devices.

Next, at step 440, the crowd sourcing data analysis system 301aggregates the crowd sourced data and performs an analysis based uponthe crowd sourced data. This analysis includes automatic monitoring forcertain conditions and generating alerts when certain conditions aredetected. For example, the monitoring might include determining whetherconcentrations of devices rise above threshold levels. The thresholdsmight be related to a location within the venue, such that if more thana certain number of individuals are within a predetermined area of thevenue then an alert is generated. Also, for example, the monitoringmight include determining when device speeds rise over a threshold for agiven number of devices. Also for example, the monitoring might includedetermining when device heights drop below a threshold for a give numberof devices. Providing the monitoring and alerts allows the crowdsourcing data analysis system 301 (or the event staff) to proactivelyaddress issues in real time. For example, the crowd sourcing dataanalysis system 301 might automatically turn on emergency systems suchas emergency light or sprinkler systems in response to a particularalert. The crowd sourcing data analysis system 301 might alsoautomatically open emergency exits and/or close fire doors in responseto a particular alert. The crowd sourcing data analysis system 301 mightalso automatically request emergency assistance in response to aparticular alert.

Additionally, in certain embodiments, the analysis tailored to the dataprovided by the crowd sourced devices. For example, if the crowd sourceddevices provided temperature data, then the crowd sourced data analysissystem 301 could perform a heat map analysis using the temperatureprovided by the plurality of crowd sourced devices.

Next, at step 450, after the event is completed, the crowd sourceddevices are unregistered. In certain embodiments when the crowd sourceddevices were provided by the event organizers, the crowd sourced devicesmay also be reclaimed upon completion of the event.

Next, at step 460, the crowd sourcing data analysis system 301 performsa post event analysis of the crowd sourced data received during theevent. This post event analysis may be used to provide recommendationsfor future events. This post event analysis may also be performed by themaster crowd sourcing data analysis system 322 and stored to the mastercrowd sourcing analysis data repository 324 for use in generating moreaccurate analyses of future events across a plurality of disparatevenues which use the crowd sourcing data analysis system 301.

FIG. 5 shows a graphical representation 500 of the operation of a crowdsourcing device analysis system in a venue. FIG. 6 shows anothergraphical representation 600 of the operation of a crowd sourcing deviceanalysis system in a venue.

More specifically, a representation of the venue 510 includesrepresentations of relevant items within the venue such as arepresentation of a stage 520 as well as representations of each of aplurality of exits 530. Information relating to the venue is storedwithin the crowd sourcing data analysis system 301. In certainembodiments, the information includes physical information such asdimensions of the venue as well as safety information such as safetyequipment (such as fire alarms, sprinkler systems, etc.) included withinthe venue. In certain embodiments, the information of the venue isdownloaded from the master crowd sourcing data analyses system 322. Incertain embodiments, information gathered from similar venues (e.g.,venues having substantially (e.g., +/−10%) the same physical size,substantially (e.g., +/−10%) the same capacity or a similar layout isalso provided to the crowd sourcing data analysis system 301.

Some or all of the individuals 540 attending the event at the venue haveassociated crowd sourced data devices. When performing the analysis ofthe event, if there are no potential issues detected from the analysisof the data received from the plurality of crowd sourced dataindividuals within a venue are represented as green dots on therepresentation of the venue 510. If some individuals are exhibiting somenegative behavior as recorded or indicated by their sensor data, thepresentation corresponding to those individuals is represented as yellowdots (e.g., individuals 610). If a serious problem with a cohort orgroup is detected based upon the analysis of the crowd sourced data froma plurality of individuals, then the presentation corresponding to thoseindividuals is represented as red (e.g., individuals 620). In certainembodiments, because more individuals would likely be involved in theserious problem, the red presentation is also more visible because moredots are represented as red. Also, in certain embodiments, an additionalwarning (such as flashing of the red dots or a separate warningindication which could include a visual presentation and/or an audioindication) is generated. For example, in the example shown in FIG. 6 anadditional warning 635 is generated with respect to the exits 630because the crowd sourced device data is indicating that the patrons arebunching at these exits and none have passed through the exits,indicating an additional potential problem with these portions of thevenue.

The present invention is well adapted to attain the advantages mentionedas well as others inherent therein. While the present invention has beendepicted, described, and is defined by reference to particularembodiments of the invention, such references do not imply a limitationon the invention, and no such limitation is to be inferred. Theinvention is capable of considerable modification, alteration, andequivalents in form and function, as will occur to those ordinarilyskilled in the pertinent arts. The depicted and described embodimentsare examples only, and are not exhaustive of the scope of the invention.

Consequently, the invention is intended to be limited only by the spiritand scope of the appended claims, giving full cognizance to equivalentsin all respects.

What is claimed is:
 1. A computer-implementable method for performing acrowdsourcing data analysis operation, comprising: receiving data from aplurality of crowd sourced devices; aggregating the data received fromthe plurality of crowd sourced devices; and, mapping the data receivedfrom the plurality of crowd sourced devices to a cohort.
 2. The methodof claim 1, further comprising: analyzing the data received from theplurality of crowd sourced devices to provide a deterministic analysisof the data received from the plurality of crowd sourced devices, thedeterministic analysis enabling an inference of a likelihood ofpotential incidents related to a group of individuals at any given time.3. The method of claim 2, further comprising: presenting a visual cue toillustrate the analysis of the data received from the plurality of crowdsourced data via a graphical representation.
 4. The method of claim 1,wherein: the mapping of the data received from the plurality of crowdsourced devices includes binding the data to a venue.
 5. The method ofclaim 4, wherein: the venue comprises at least one of a physicallocation and a logical location.
 6. The method of claim 4, furthercomprising: registering each of the plurality of crowd sourced devicesas each device enters the venue; and, deregistering each of theplurality of crowd sourced devices as each device exits the venue.